R-MaS3N: Robust Mapping of Spiking Neural Networks to 3D-NoC-Based Neuromorphic Systems for Enhanced Reliability

Abstract

Neuromorphic computing utilizes spiking neural networks (SNNs) to offer power/energy-efficient solutions for complex machine-learning problems in hardware. However, neural circuits are prone to faults caused by variability in the manufacturing flow, process variations, and manufacturing defects. This work proposes a mapping approach, R-MaS3N, that leverages the reuse of existing neurons for robust mapping of SNNs to a 3D-NoC-based neuromorphic system (NR-NASH). A heuristic-based partitioning technique is employed to partition neurons in the layers of an SNN application using neuron firing patterns. Moreover, a neuronal partitioning approach cluster mapped neurons in the layers of the neuromorphic neural circuits based on connectivity patterns and spiking activities. Evaluation results show that the proposed fault-tolerant mapping method maintains a remapping efficiency of 100% with a fault rate of 40% in the 3D NoC-based neuromorphic system. With a NoC system configuration of $4\times 4\times 4$ and 256 neurons per cluster, our approach has a remapping time of $71\times $ less than the previous approach with the same NoC system configuration parameters. In addition, the mean time to failure (MTTF) of the mapping method for system configuration $5\times 5\times 5$ NoC size at a 40% fault rate surpasses the previous method at 20% fault rate by 16% for $4\times 4\times 4$ NoC size.

Keywords

• Reliable neuromorphic
• mapping
• neural reuse
• 3D-NoC
• clustering